Paper Info
Title
Recognition of Similar Activities Based on Activity Relationship
Abstract
With the Internet of Things development, recognizing activities of daily living usually deploy many sensors to objects and environments. However, the sensor events can be triggered by many activities which decreases the accuracy or even fails of inference. In addition, a huge number of personalized activities takes up a lot of space resources and reduces the readability of model. Therefore, this paper adds the duration and period characteristics to improve the inference performance, then adopts the structural model to increase the expendability and standardization. We show that the similar activities inference accuracy has been improved by an average of 8 times. The operating time has been decreased by an average of 0.36 times. This inference method has the good performance and can be used in the future for activity recognition.
Year
DOI
Venue
2019
10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00078
2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
Keywords
DocType
ISBN
activity recognition,activity relationship,markov logic network
Conference
978-1-7281-4035-3
Citations 
PageRank 
References 
0
0.34
19
Authors
4
Name
Order
Citations
PageRank
Qingjuan Li1142.75
Huansheng Ning284783.48
Ismini Psychoula3143.05
Liming Chen42607201.71